Glaucoma is a group of optic nerve disorders characterized by progressive optic nerve atrophy and visual field defects, which can lead to irreversible blindness. Early diagnosis of glaucoma is essential for preventing visual loss. However, due to the absence of obvious early symptoms, the diagnosis of glaucoma remains challenging. Visual electrophysiological examinations, an objective approach for evaluating visual function, have the potential to be used in the early diagnosis of glaucoma. This review integrates the latest publications to introduce visual electrophysiological examination techniques, including electroretinography(ERG)and visual evoked potential(VEP). It also explores the mechanisms underlying these techniques and their application value in the early diagnosis of glaucoma. In addition, this review summarizes the advantages, limitations, and applicable scenarios of different visual electrophysiological techniques. Finally, the review provides an outlook on the development prospects of visual electrophysiological techniques in the early diagnosis of glaucoma. The findings of this review can assist clinicians in selecting appropriate diagnostic methods, promote the innovation and development of early visual electrophysiological diagnostic techniques for glaucoma, and contribute to reducing the risk of blindness caused by glaucoma.

Optical imaging is highly valued for its superior temporal and spatial resolution. This is particularly important in near-infrared II (NIR-II, 1 000-3 000 nm) imaging, which offers advantages such as reduced tissue absorption, minimal scattering, and low autofluorescence. These characteristics make NIR-II imaging especially suitable for deep tissue visualization, where high contrast and minimal background interference are critical for accurate diagnosis and monitoring. Currently, inorganic fluorescent probes—such as carbon nanotubes, rare earth nanoparticles, and quantum dots—offer high brightness and stability. However, they are hindered by ambiguous structures, larger sizes, and potential accumulation toxicity in vivo. In contrast, organic fluorescent probes, including small molecules and polymers, demonstrate higher biocompatibility but are limited by shorter emission wavelengths, lower quantum yields, and reduced stability. Recently, gold clusters have emerged as a promising class of nanomaterials with potential applications in biocatalysis, fluorescence sensing, biological imaging, and more. Water-soluble gold clusters are particularly attractive as fluorescent probes due to their remarkable optical properties, including strong photoluminescence, large Stokes shifts, and excellent photostability. Furthermore, their outstanding biocompatibility—attributed to good aqueous stability, ultra-small hydrodynamic size, and high renal clearance efficiency—makes them especially suitable for biomedical applications. Gold clusters hold significant potential for NIR-II fluorescence imaging. Atomic-precision gold clusters, typically composed of tens to hundreds of gold atoms and measuring only a few nanometers in diameter, possess well-defined three-dimensional structures and clear spatial coordination. This atomic-level precision enables fine-tuned structural regulation, further enhancing their fluorescence properties. Variations in cluster size, surface ligands, and alloying elements can result in distinct physicochemical characteristics. The incorporation of different atoms can modulate the atomic and electronic structures of gold clusters, while diverse ligands can influence surface polarity and steric hindrance. As such, strategies like alloying and ligand engineering are effective in enhancing both fluorescence and catalytic performance, thereby meeting a broader range of clinical needs. In recent years, gold clusters have attracted growing attention in the biomedical field. Their application in NIR-II imaging has led to significant progress in vascular, organ, and tumor imaging. The resulting high-resolution, high signal-to-noise imaging provides powerful tools for clinical diagnostics. Moreover, biologically active gold clusters can aid in drug delivery and disease diagnosis and treatment, offering new opportunities for clinical therapeutics. Despite the notable achievements in fundamental research and clinical translation, further studies are required to address challenges related to the standardized synthesis and complex metabolic behavior of gold clusters. Resolving these issues will help accelerate their clinical adoption and broaden their biomedical applications.

ObjectiveTobacco-related diseases remain one of the leading preventable public health challenges worldwide and are among the primary causes of premature death. In recent years, accumulating evidence has supported the classification of nicotine addiction as a chronic brain disease, profoundly affecting both brain structure and function. Despite the urgency, effective diagnostic methods for smoking addiction remain lacking, posing significant challenges for early intervention and treatment. To address this issue and gain deeper insights into the neural mechanisms underlying nicotine dependence, this study proposes a novel graph neural network framework, termed TI-GNN. This model leverages functional magnetic resonance imaging (fMRI) data to identify complex and subtle abnormalities in brain connectivity patterns associated with smoking addiction. MethodsThe study utilizes fMRI data to construct functional connectivity matrices that represent interaction patterns among brain regions. These matrices are interpreted as graphs, where brain regions are nodes and the strength of functional connectivity between them serves as edges. The proposed TI-GNN model integrates a Transformer module to effectively capture global interactions across the entire brain network, enabling a comprehensive understanding of high-level connectivity patterns. Additionally, a spatial attention mechanism is employed to selectively focus on informative inter-regional connections while filtering out irrelevant or noisy features. This design enhances the model’s ability to learn meaningful neural representations crucial for classification tasks. A key innovation of TI-GNN lies in its built-in causal interpretation module, which aims to infer directional and potentially causal relationships among brain regions. This not only improves predictive performance but also enhances model interpretability—an essential attribute for clinical applications. The identification of causal links provides valuable insights into the neuropathological basis of addiction and contributes to the development of biologically plausible and trustworthy diagnostic tools. ResultsExperimental results demonstrate that the TI-GNN model achieves superior classification performance on the smoking addiction dataset, outperforming several state-of-the-art baseline models. Specifically, TI-GNN attains an accuracy of 0.91, an F1-score of 0.91, and a Matthews correlation coefficient (MCC) of 0.83, indicating strong robustness and reliability. Beyond performance metrics, TI-GNN identifies critical abnormal connectivity patterns in several brain regions implicated in addiction. Notably, it highlights dysregulations in the amygdala and the anterior cingulate cortex, consistent with prior clinical and neuroimaging findings. These regions are well known for their roles in emotional regulation, reward processing, and impulse control—functions that are frequently disrupted in nicotine dependence. ConclusionThe TI-GNN framework offers a powerful and interpretable tool for the objective diagnosis of smoking addiction. By integrating advanced graph learning techniques with causal inference capabilities, the model not only achieves high diagnostic accuracy but also elucidates the neurobiological underpinnings of addiction. The identification of specific abnormal brain networks and their causal interactions deepens our understanding of addiction pathophysiology and lays the groundwork for developing targeted intervention strategies and personalized treatment approaches in the future.

Objective To summarize the epidemiological and clinical features of infectious diseases of the central nervous system(CNS)by a single-center analysis.Methods A retrospective analysis was conducted on the data of 1247 cases of CNS infectious diseases diagnosed and treated in the First Medical Center of PLA General Hospital from 2001 to 2020.Results The data for this group of CNS infectious diseases by disease type in descending order of number of cases were viruses 743(59.6％),Mycobacterium tuberculosis 249(20.0％),other bacteria 150(12.0％),fungi 68(5.5％),parasites 18(1.4％),Treponema pallidum 18(1.4％)and rickettsia 1(0.1％).The number of cases increased by 177 cases(33.1％)in the latter 10 years compared to the previous 10 years(P<0.05).No significant difference in seasonal distribution pattern of data between disease types(P>0.05).Male to female ratio is 1.87︰1,mostly under 60 years of age.Viruses are more likely to infect students,most often at university/college level and above,farmers are overrepresented among bacteria and Mycobacterium tuberculosis,and more infections of Treponema pallidum in workers.CNS infectious diseases are characterized by fever,headache and signs of meningeal irritation,with the adductor nerve being the more commonly involved cranial nerve.Matagenomic next-generation sequencing improves clinical diagnostic capabilities.The median hospital days for CNS infectious diseases are 18.00(11.00,27.00)and median hospital costs are ￥29,500(￥16,000,￥59,200).The mortality rate from CNS infectious diseases is 1.6％.Conclusions The incidence of CNS infectious diseases is increasing last ten years,with complex clinical presentation,severe symptoms and poor prognosis.Early and accurate diagnosis and standardized clinical treatment can significantly reduce the morbidity and mortality rate and ease the burden of disease.

Sonogenetics is an emerging synthetic biology technique that uses sound waves to activate mechanosensitive ion channel proteins on the cell surface to regulate cell behavior and function.Due to the widespread presence of mechanically sensitive ion channel systems in cells and the advantages of non-invasion,strong penetrability,high safety and high accuracy of sonogenetics technology,it has great development potential in basic biomedical research and clinical applications,especially in neuronal regulation,tumor mechanism research,sonodynamic therapy and hearing impairment.This review discusses the basic principles of sonogenetics,the development status of sonogenetics and its application in the prevention and treatment of noise-induced hearing loss,summarizes and analyzes the current challenges and future development direction,thus providing a reference for further research and development of sonogenetics in the field of military medicine.

Veno-arterial extracorporeal membrane oxygenation is an effective method to reduce perioperative adverse events such as cardiogenic shock in patients undergoing complex high-risk indicated percutaneous coronary intervention.Femoral artery and femoral vein are the main routes for conventional veno-arterial extracorporeal membrane oxygenation in China,while the cases of extracorporeal membrane oxygenation insertion via axillary artery are relatively rare.However,the axillary artery intubation veno-arterial extracorporeal membrane oxygenation assisted mode has been regarded as one of the routine clinical paths for the treatment of critically ill patients in foreign countries.This paper reports a case of an elderly male patient who underwent high risk and complex percutaneous coronary interventional therapy by right axillary artery implantation with extracorporeal membrane oxygenation assisted circulation due to the difficulty of femoral artery approach.In order to provide reference for the selection of clinical extracorporeal membrane oxygenation technique route.

Objective To explore the risk factors related to afterload mismatch(AM)after transcatheter mitral valve repair(MitraClip).Methods This was a retrospective cohort study.48 patients hospitalized in the Department of Cardiovascular Medicine,the First Affiliated Hospital of Sun Yat-sen University from December 2021 to December 2023,who underwent MitraClip due to severe mitral regurgitation(MR)were included.Preoperative clinical data,laboratory tests,and preoperative and postoperative color Doppler echocardiographic examination results of surgical patients were collected.AM was defined as the left ventricular ejection fraction(LVEF)decreased by 15％or more after surgery compared with the one before(dLVEF≤-15％).Patients were divided into AM group and non-AM group according to whether afterload mismatch occurred.Univariate and multivariate logistic regression were used to analyze the risk factors of postoperative AM.Results Among 48 patients who underwent MitraClip,14 of them(29.2％)developed afterload-mismatched.For those without AM,their overall LVEF was improved after the operation;for patients in both AM group and non-AM group,their overall left ventricular end-diastolic diameter(LVEDd),left ventricular end-diastolic diameter volume index(LVEDVi)was reduced compared with the preoperative ones.Univariate regression analysis showed that C-reactive protein levels(OR 1.98,95％CI 1.02-3.83),platelets(OR 2.22,95％CI 1.08-4.53),systemic immune inflammation index(OR 1.96,95％CI 1.03-3.71)were associated with an increased risk of AM in patients undergoing MitraClip(all P＜0.05),while those with larger right atrial diameter(OR 0.35,95％CI 0.13-0.93)or moderate to severe tricuspid regurgitation(OR 0.19,95％CI 0.05-0.81)were less likely to develop into AM(both P＜0.05),which is still satisfied after adjustment.Conclusions For patients who underwent MitraClip,C-reactive protein levels,platelets and systemic immune inflammation index(SII)are associated with an increased risk of afterload mismatched,while those with larger right atrial diameter or moderate to severe tricuspid regurgitation were less likely to develop into AM.